remaining = (FLAGS.EPOCH * dataset.train_batch_num -
step) * FLAGS.BATCH_SIZE / rate
print(
"###################################################")
print(
"progress epoch %d step %d / %d image/sec %0.1f remaining %0.1fm"
% (epoch, num, dataset.train_batch_num, rate,
remaining / 60))
print("- Loss =", loss)
print("- Weighted RMSE on validation =", valid_eval)
print(
"- Accuracy on validation =",
np.sum(valid_y == valid_predict) /
np.shape(valid_y)[0])
print("- Best(but not saved) Weight RMSE on validation =",
saved_eval)
print("- Best model on validation at epoch =", saved_epoch,
"step =", saved_num)
print("- Min kp-index on validation set :",
np.min(valid_predict))
print("- Max kp-index on validation set :",
np.max(valid_predict))
print("Finish!")
if __name__ == '__main__':
FLAGS = Config()
FLAGS.MODE = 'train'
main(FLAGS)
# Define label and model output.
outputs = model.get_outputs()
last_output = outputs[-1]
output = tf.nn.softmax(last_output)
output_class = tf.argmax(output, 1)
tf_config = tf.ConfigProto(allow_soft_placement=True)
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(FLAGS.MODEL_PATH))
test_x = testset.X_test
test_predict = sess.run(output_class,
feed_dict={model._inputs: test_x})
f = open('output.csv', 'w', encoding='utf-8')
wr = csv.writer(f)
for date in np.arange(365):
row_list = test_predict[date * 8:(date + 1) * 8]
wr.writerow(row_list)
f.close()
print("Finished!")
if __name__ == '__main__':
FLAGS = Config()
FLAGS.MODE = 'test'
main(FLAGS)
